Development of Human Support Robot as the research platform of a domestic mobile manipulator

Yamamoto, Takashi; Terada, Koji; Ochiai, Akiyoshi; Saito, Fuminori; Asahara, Yoshiaki; Murase, Kazuto

doi:10.1186/s40648-019-0132-3

Cited by 192 publications

(79 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The robot experiments are performed with the Toyota Human Support Robot (Yamamoto et al, 2019). The platform consists of a 4-DOF arm but motions are computed including the omni-directional base, which effectively offers seven degrees of freedom.…”

Section: Methodsmentioning

confidence: 99%

DGCM-Net: Dense Geometrical Correspondence Matching Network for Incremental Experience-Based Robotic Grasping

2020

View full text Add to dashboard Cite

This article presents a method for grasping novel objects by learning from experience. Successful attempts are remembered and then used to guide future grasps such that more reliable grasping is achieved over time. To transfer the learned experience to unseen objects, we introduce the dense geometric correspondence matching network (DGCM-Net). This applies metric learning to encode objects with similar geometry nearby in feature space. Retrieving relevant experience for an unseen object is thus a nearest neighbor search with the encoded feature maps. DGCM-Net also reconstructs 3D-3D correspondences using the view-dependent normalized object coordinate space to transform grasp configurations from retrieved samples to unseen objects. In comparison to baseline methods, our approach achieves an equivalent grasp success rate. However, the baselines are significantly improved when fusing the knowledge from experience with their grasp proposal strategy. Offline experiments with a grasping dataset highlight the capability to transfer grasps to new instances as well as to improve success rate over time from increasing experience. Lastly, by learning task-relevant grasps, our approach can prioritize grasp configurations that enable the functional use of objects.

show abstract

Section: Methodsmentioning

confidence: 99%

DGCM-Net: Dense Geometrical Correspondence Matching Network for Incremental Experience-Based Robotic Grasping

2020

View full text Add to dashboard Cite

show abstract

“…The model was applied to a neurorobotics demonstration to show how schemas are formed and used in real-world environments (Hwu et al, 2020). The Toyota Human Support Robot (HSR) used the schema model to encode schemas in two separate rooms (Yamamoto et al, 2019), learning to retrieve objects from both rooms (see Figure 4C). Despite the fact that some objects were present in both rooms, the robot retrieved the one consistent with its current schema, and did not get confused when switching between tasks separately trained in each of the schemas.…”

Section: Schemas and Consolidationmentioning

confidence: 99%

Neurorobots as a Means Toward Neuroethology and Explainable AI

et al. 2020

View full text Add to dashboard Cite

Understanding why deep neural networks and machine learning algorithms act as they do is a difficult endeavor. Neuroscientists are faced with similar problems. One way biologists address this issue is by closely observing behavior while recording neurons or manipulating brain circuits. This has been called neuroethology. In a similar way, neurorobotics can be used to explain how neural network activity leads to behavior. In real world settings, neurorobots have been shown to perform behaviors analogous to animals. Moreover, a neuroroboticist has total control over the network, and by analyzing different neural groups or studying the effect of network perturbations (e.g., simulated lesions), they may be able to explain how the robot's behavior arises from artificial brain activity. In this paper, we review neurorobot experiments by focusing on how the robot's behavior leads to a qualitative and quantitative explanation of neural activity, and vice versa, that is, how neural activity leads to behavior. We suggest that using neurorobots as a form of computational neuroethology can be a powerful methodology for understanding neuroscience, as well as for artificial intelligence and machine learning.

show abstract

“…The simulator environment was SIGVerse version 3.0 [31], a client-server based architecture that can connect the robot operating system (ROS) [32] and Unity. The virtual robot model in SIGVerse was Toyota's Human Support Robot (HSR) [33]. The diameter of the HSR is ϕ430 mm.…”

Section: Conditionmentioning

confidence: 99%

Spatial concept-based navigation with human speech instructions via probabilistic inference on Bayesian generative model

et al. 2020

View full text Add to dashboard Cite

Robots are required to not only learn spatial concepts autonomously but also utilize such knowledge for various tasks in a domestic environment. Spatial concept represents a multimodal place category acquired from the robot's spatial experience including vision, speech-language, and selfposition. The aim of this study is to enable a mobile robot to perform navigational tasks with human speech instructions, such as 'Go to the kitchen', via probabilistic inference on a Bayesian generative model using spatial concepts. Specifically, path planning was formalized as the maximization of probabilistic distribution on the path-trajectory under speech instruction, based on a control-asinference framework. Furthermore, we described the relationship between probabilistic inference based on the Bayesian generative model and control problem including reinforcement learning. We demonstrated path planning based on human instruction using acquired spatial concepts to verify the usefulness of the proposed approach in the simulator and in real environments. Experimentally, places instructed by the user's speech commands showed high probability values, and the trajectory toward the target place was correctly estimated. Our approach, based on probabilistic inference concerning decision-making, can lead to further improvement in robot autonomy.

show abstract

Development of Human Support Robot as the research platform of a domestic mobile manipulator

Cited by 192 publications

References 22 publications

DGCM-Net: Dense Geometrical Correspondence Matching Network for Incremental Experience-Based Robotic Grasping

DGCM-Net: Dense Geometrical Correspondence Matching Network for Incremental Experience-Based Robotic Grasping

Neurorobots as a Means Toward Neuroethology and Explainable AI

Spatial concept-based navigation with human speech instructions via probabilistic inference on Bayesian generative model

Contact Info

Product

Resources

About